Artificial intelligence (AI) is a powerful tool—but without the right system-wide architecture in place to support your initiatives, your organization is leaving value on the table. Featuring interactive exercises, industry speakers, and a hands-on group project, this dynamic five-day course is designed to equip you with the skills and strategies you need to deploy an AI systems engineering approach that maximizes the value of your digital products and services.
This course may be taken as a standalone program or as part of the Professional Certificate Program in Machine Learning & Artificial Intelligence. COMPLETING THE COURSE WILL CONTRIBUTE 5 DAYS TOWARDS THE CERTIFICATE.
AI is revolutionizing many industries, including energy, consumer products and services, automotive, financial services, national security, healthcare, and advertising. But too often, business and IT leaders take a limited view of AI, focusing almost exclusively on machine learning (ML) methods. But AI technologies are, in fact, key enablers to complex systems. They require not only ML technologies, but also trustworthy data sensors and sources, appropriate data conditioning processes, responsible governance frameworks, and a balance between human and machine interactions. In short, organizations must evolve into a systems engineering mindset to optimize their AI investments.
This program will equip professionals to lead, develop, and deploy AI systems in responsible ways that augment human capabilities. Taking a broader, holistic perspective, it emphasizes an AI system architecture approach applied to products and services and provides techniques for transitioning from development into operations. To get the most of your AI initiatives, you must consider the entire ecosystem surrounding your AI systems and then recruit and retain talented multi-disciplinary teams to be successful.
Over five days, you will examine the trade-offs between roles best suited to humans vs. machines and develop the skills you need to lead and manage high-technology teams. Through interactive exercises and lectures, you will acquire practical experience building ML models using Jupyter Notebook, and master 10 principles for incorporating people, processes, and technologies in the successful deployment of AI products and/or services. You will also explore what makes GPUs and TPUs well-matched to executing machine learning algorithms.
Unique to this course, we'll host AI practitioners to share their experiences, AI journey, and importance of taking a holistic approach to AI from architecture principles to deployment. We will include, in the lecture series, a short primer on Large Language Models (LLMs).
Upon completion of this program, you will have the skills to understand the AI fundamentals necessary to develop end-to-end systems, lead AI teams, and successfully deploy AI capabilities.
Participants are encouraged to purchase the electronic publication of our upcoming book titled - Artificial Intelligence: A Systems Approach from Architecture Principles to Deployment published by the MIT Press.
NOTE: This is not a programming course. While coding is touched on, it is not a significant component of the curriculum.
Certificate of Completion from MIT Professional Education
This program is ideal for professionals who are responsible for the successful deployment of AI capabilities and technologies. Participants are not required to have a deep technical background, but having some familiarity with AI concepts will be helpful. Within this range, roles that will benefit from the course curriculum include, but are not limited to:
- Systems engineers who need practical frameworks for building AI pipelines and launching new projects
- Executives looking to manage change and make smart investment decisions related to AI technology
- Technical leaders who require specialized knowledge to head effective AI-powered teams
- Product directors who want to develop cohesive plans for product development and deployment
- Team leaders who guide high-technology groups in executing complex AI initiatives
- Researchers who want to explore and advance AI applications across industries
- Entrepreneurs who need actionable roadmaps for building and growing AI-powered businesses
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